Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer

STRI

書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡(luò)公開度




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋學(xué)科排名

光滑

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620544.jpg

SLAY

CRUE

SPURN

Fast Redescription Mining Using Locality-Sensitive Hashing we present new algorithms that perform the matching and extension orders of magnitude faster than the existing approaches. Our algorithms are based on locality-sensitive hashing with a tailored approach to handle the discretisation of numerical attributes as used in redescription mining.

predict

figure

Conference proceedings 2024scovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024...?..The papers presented in these proceedings are from the following three conference tracks: -..Research Track:.?The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 su

LAIR

emulsify

Model-Based Reinforcement Learning with?Multi-task Offline Pretrainingsferring the task-agnostic knowledge of physical dynamics to facilitate world model training, and (ii) learning to replay relevant source actions to guide the target policy. We demonstrate the advantages of our approach compared with the state-of-the-art methods in Meta-World and DeepMind Control Suite.

LIMN